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P3DL: A Privacy Preserving Personalized Distributed Learning Framework for EEG-Based Cognitive State Identification.
IEEE Journal of Biomedical and Health Informatics
|October 9, 2025
Summary
This study introduces a privacy-preserving framework for identifying cognitive states in the elderly using electroencephalography (EEG). The new method enhances accuracy while protecting sensitive brain data.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Healthcare Technology
Background:
- Electroencephalography (EEG) is crucial for identifying cognitive decline in the elderly.
- EEG data contains sensitive personal information, posing privacy risks.
- Current methods prioritize accuracy over EEG data privacy.
Purpose of the Study:
- To develop a privacy-preserving personalized distributed learning framework (P3DL) for EEG-based cognitive state identification.
- To enhance both the accuracy and privacy of cognitive assessment in the elderly.
Main Methods:
- Proposed a privacy-preserving personalized distributed learning framework (P3DL) with clients and a central server.
- Implemented a federated dynamic update strategy (FedDBS) for model optimization.
- Introduced a novel loss function, extreme error Loss (E2Loss), to improve identification and misdiagnosis assessment.
Main Results:
- P3DL demonstrated an average increase in F2Score of 5.58% and 3.31% on clinical and public datasets, respectively.
- Accuracy improved by 1.78% and 2.46% on the respective datasets.
- Framework scalability was confirmed in emotion recognition tasks.
Conclusions:
- The P3DL framework effectively enhances cognitive state identification accuracy.
- P3DL ensures the privacy protection of sensitive EEG data.
- This work opens new avenues for secure and reliable healthcare applications using EEG.
